A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi‐strategy self‐optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full‐rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition‐based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self‐learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine‐exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.